This document contains figures and statistics used for Time-Frequency Representation (TFR) analyses.

source('ana/shared.R')
source('ana/permutationTtest.R')

Error Feedback Processing

We transform the signals across epochs into time-frequency representations (TFRs) using Morlet wavelet (6 cycles) convolution. The frequencies we include are log-spaced values that range from 6 to 35 Hz. First, we show plots time-locked to feedback onset. We include 250 ms before the feedback onset, to show TFRs around the movement onset. However, we focus our statistical analyses for the second that follows feedback onset.

Early vs. Late conditions

We define early and late conditions in a similar manner to how we defined these conditions in our ERP analyses. That is, we consider the first two blocks of rotation and mirror training (first 12 trials) as early training and the last six blocks (36 trials) as late training. For the random rotation, we combined both random rotation sets as they do not show differences in performance. From this combined set of trials, the first two blocks (12 trials) are considered as early training, while the last 4 blocks (24 trials) are considered as late training.

We then generate TFRs for each of the conditions (early vs. late for fixed rotation, mirror reversal, random rotation), and calculate TFRs for the baseline aligned reaches (48 trials). We focused on two different regions of interest (ROIs): the medial frontal areas (F1, Fz, F2, FC1, FCz, FC2, C1, Cz, C2) and lateral central areas of the left hemisphere (i.e., opposite the moving hand; C5, C3, CP5, CP3, CP1, P5, P3, P1).

Time-Frequency Representations

Medial frontal areas

Lateral central areas

Frequency bands compared to aligned baseline

For each ROI, we calculate the mean power (µV²) within each participant of the following frequency bands: theta (6-8 Hz), alpha (9-13 Hz), and beta (13-25 Hz). We then compare these mean frequencies between early and late training for the different perturbation types.

First, we compare each early or late condition to the aligned baseline condition. For statistical analyses, we implemented a cluster-based permutation t-test. Clusters of time points that exceed the t-value threshold (determined by a t-distribution, given a p-value of 0.05 and sample size of 32) will be shown in light orange or red colors, while clusters of time points that significantly differ from chance after 1000 permutations will be shown in dark orange or red colors.

Theta band
plotPermTestEarlyLateTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestEarlyLateTFRs(freqs = 'theta', roi = 'latcen')

Alpha band
plotPermTestEarlyLateTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestEarlyLateTFRs(freqs = 'alpha', roi = 'latcen')

Beta band
plotPermTestEarlyLateTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestEarlyLateTFRs(freqs = 'beta', roi = 'latcen')

Statistics
getEarlyLateTFRPvalStats(comparison = 'vsAligned', erps = 'frn', roi = 'medfro')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_earlyrot             188           201      0.940    1.000    0.222
## 2   1  theta_laterot              46            74      0.230    0.365    0.135
## 3   2  theta_laterot             110           181      0.550    0.900    0.025
## 4   3 theta_earlyrdm               0            54      0.000    0.265    0.019
## 5   4 theta_earlyrdm              84           146      0.420    0.725    0.027
## 6   5 theta_earlyrdm             165           201      0.825    1.000    0.066
## 7   6  theta_laterdm               2           101      0.010    0.500    0.018
## 8   7 theta_earlymir               0            49      0.000    0.240    0.039
## 9   8 theta_earlymir             194           201      0.970    1.000    0.197
## 10  9  theta_latemir              14            87      0.070    0.430    0.032
## 11 10 alpha_earlyrot              15            17      0.075    0.080    0.301
## 12 11 alpha_earlyrot             192           201      0.960    1.000    0.244
## 13 12  alpha_laterot              NA            NA         NA       NA       NA
## 14 13 alpha_earlyrdm              58           102      0.290    0.505    0.058
## 15 14 alpha_earlyrdm             121           184      0.605    0.915    0.020
## 16 15  alpha_laterdm              NA            NA         NA       NA       NA
## 17 16 alpha_earlymir              19            45      0.095    0.220    0.124
## 18 17 alpha_earlymir             173           192      0.865    0.955    0.162
## 19 18  alpha_latemir              21            23      0.105    0.110    0.229
## 20 19  beta_earlyrot              NA            NA         NA       NA       NA
## 21 20   beta_laterot              36            46      0.180    0.225    0.331
## 22 21  beta_earlyrdm              37           123      0.185    0.610    0.002
## 23 22  beta_earlyrdm             152           174      0.760    0.865    0.121
## 24 23   beta_laterdm              NA            NA         NA       NA       NA
## 25 24  beta_earlymir               7             9      0.035    0.040    0.373
## 26 25  beta_earlymir              97           152      0.485    0.755    0.011
## 27 26   beta_latemir               8            29      0.040    0.140    0.123
getEarlyLateTFRPvalStats(comparison = 'vsAligned', erps = 'frn', roi = 'latcen')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_earlyrot              NA            NA         NA       NA       NA
## 2   1  theta_laterot             101           192      0.505    0.955    0.013
## 3   2 theta_earlyrdm               9            37      0.045    0.180    0.152
## 4   3 theta_earlyrdm              90           201      0.450    1.000    0.001
## 5   4  theta_laterdm               0            19      0.000    0.090    0.201
## 6   5  theta_laterdm              28            81      0.140    0.400    0.039
## 7   6 theta_earlymir               0             1      0.000    0.000    0.233
## 8   7 theta_earlymir             103           201      0.515    1.000    0.013
## 9   8  theta_latemir              NA            NA         NA       NA       NA
## 10  9 alpha_earlyrot              NA            NA         NA       NA       NA
## 11 10  alpha_laterot             110           157      0.550    0.780    0.061
## 12 11 alpha_earlyrdm              38           178      0.190    0.885    0.001
## 13 12 alpha_earlyrdm             197           201      0.985    1.000    0.260
## 14 13  alpha_laterdm              NA            NA         NA       NA       NA
## 15 14 alpha_earlymir              61            70      0.305    0.345    0.255
## 16 15 alpha_earlymir             175           189      0.875    0.940    0.210
## 17 16  alpha_latemir               0            71      0.000    0.350    0.023
## 18 17  beta_earlyrot              14            22      0.070    0.105    0.283
## 19 18   beta_laterot              NA            NA         NA       NA       NA
## 20 19  beta_earlyrdm              60           112      0.300    0.555    0.018
## 21 20   beta_laterdm              NA            NA         NA       NA       NA
## 22 21  beta_earlymir             113           173      0.565    0.860    0.027
## 23 22   beta_latemir              17            25      0.085    0.120    0.248

Time-Frequency Representations (Early vs. Late Difference Waves)

We then calculate the TFR differences between each early or late condition and the aligned condition. Then we compare early from late in each of the perturbation types.

Medial frontal areas

Lateral central areas

Frequency bands comparing differences between early and late training

Theta band
plotPermTestEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

For the medial frontal areas, we observe differences between early and late in the random perturbation. This difference occurs around 300 ms up to 1 second following feedback onset.

For the lateral central areas, we observe a difference in the random and mirror perturbations, where we observe early training to be more negative than the late condition.

Alpha band
plotPermTestEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

For the medial frontal areas, we do not find any significant clusters.

For the lateral central areas, we observe a difference between early and late training in the rotation perturbation, where late training is more positive around 500 ms to 750 ms post-feedback onset.

Beta band
plotPermTestEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

For the medial frontal areas, we find differences between early and late training for the random perturbation, with early training having more negative beta power around 250 to 500 ms post-feedback.

For the lateral central areas, we observe differences in the mirror perturbation, where early training is more negative than late, around 500 ms to 900 ms post-feedback.

Statistics
getEarlyLateTFRPvalStats(comparison = 'EarlyvsLate', erps = 'frn', roi = 'medfro')
##    X        condition clust_idx_start clust_idx_end time_start time_end
## 1  0 theta_medfro_rot             168           201      0.840    1.000
## 2  1 theta_medfro_rdm              67           201      0.335    1.000
## 3  2 theta_medfro_mir             122           140      0.610    0.695
## 4  3 alpha_medfro_rot             187           201      0.935    1.000
## 5  4 alpha_medfro_rdm             139           169      0.695    0.840
## 6  5 alpha_medfro_mir              NA            NA         NA       NA
## 7  6  beta_medfro_rot              NA            NA         NA       NA
## 8  7  beta_medfro_rdm               6            15      0.030    0.070
## 9  8  beta_medfro_rdm              39           106      0.195    0.525
## 10 9  beta_medfro_mir             104           111      0.520    0.550
##    p_values
## 1     0.104
## 2     0.005
## 3     0.158
## 4     0.249
## 5     0.120
## 6        NA
## 7        NA
## 8     0.266
## 9     0.016
## 10    0.287
getEarlyLateTFRPvalStats(comparison = 'EarlyvsLate', erps = 'frn', roi = 'latcen')
##   X        condition clust_idx_start clust_idx_end time_start time_end p_values
## 1 0 theta_latcen_rot             144           201      0.720    1.000    0.061
## 2 1 theta_latcen_rdm              73           155      0.365    0.770    0.031
## 3 2 theta_latcen_mir             105           157      0.525    0.780    0.034
## 4 3 alpha_latcen_rot             100           148      0.500    0.735    0.048
## 5 4 alpha_latcen_rdm              NA            NA         NA       NA       NA
## 6 5 alpha_latcen_mir               0            13      0.000    0.060    0.193
## 7 6  beta_latcen_rot              NA            NA         NA       NA       NA
## 8 7  beta_latcen_rdm              64           102      0.320    0.505    0.058
## 9 8  beta_latcen_mir             106           188      0.530    0.935    0.012

Time-Frequency Representations (Perturbation type comparisons)

Next, we subtract the early from the late condition, across the different perturbation types. Then, we compare each perturbation type with the other two. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves.

Medial frontal areas

Latercal central areas

Frequency bands comparing across perturbation types

Theta band
plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

We do not observe any significant clusters for both regions of interest.

Alpha band
plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

We do not observe any significant clusters for both regions of interest.

Beta band
plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We observe a significant cluster when comparing fixed and random rotation perturbations, around 250 ms post-feedback, with random rotation showing more positive activity.

Statistics
getEarlyLateTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'frn', roi = 'medfro')
##    X            condition clust_idx_start clust_idx_end time_start time_end
## 1  0 theta_medfro_rotvmir              NA            NA         NA       NA
## 2  1 theta_medfro_rotvrdm              81           129      0.405    0.640
## 3  2 theta_medfro_mirvrdm              NA            NA         NA       NA
## 4  3 alpha_medfro_rotvmir              NA            NA         NA       NA
## 5  4 alpha_medfro_rotvrdm              NA            NA         NA       NA
## 6  5 alpha_medfro_mirvrdm              NA            NA         NA       NA
## 7  6  beta_medfro_rotvmir              NA            NA         NA       NA
## 8  7  beta_medfro_rotvrdm              39            95      0.195    0.470
## 9  8  beta_medfro_rotvrdm             141           149      0.705    0.740
## 10 9  beta_medfro_mirvrdm              47            68      0.235    0.335
##    p_values
## 1        NA
## 2     0.073
## 3        NA
## 4        NA
## 5        NA
## 6        NA
## 7        NA
## 8     0.016
## 9     0.301
## 10    0.118
getEarlyLateTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'frn', roi = 'latcen')
##   X            condition clust_idx_start clust_idx_end time_start time_end
## 1 0 theta_latcen_rotvmir               0             7       0.00     0.03
## 2 1 theta_latcen_rotvrdm              NA            NA         NA       NA
## 3 2 theta_latcen_mirvrdm              NA            NA         NA       NA
## 4 3 alpha_latcen_rotvmir              NA            NA         NA       NA
## 5 4 alpha_latcen_rotvrdm              NA            NA         NA       NA
## 6 5 alpha_latcen_mirvrdm              NA            NA         NA       NA
## 7 6  beta_latcen_rotvmir              NA            NA         NA       NA
## 8 7  beta_latcen_rotvrdm              62            89       0.31     0.44
## 9 8  beta_latcen_mirvrdm              NA            NA         NA       NA
##   p_values
## 1    0.224
## 2       NA
## 3       NA
## 4       NA
## 5       NA
## 6       NA
## 7       NA
## 8    0.107
## 9       NA

Small vs. Large errors

We repeat the same analyses steps, but now compare small and large errors experienced after the reaching movement. We defined these small and large error conditions similar to how we defined them in our ERP analyses.

Time-Frequency Representations

Medial frontal areas

Lateral central areas

Frequency bands compared to aligned baseline

For each ROI, we calculate the mean power (µV²) within each participant of the following frequency bands: theta (6-8 Hz), alpha (9-13 Hz), and beta (13-25 Hz). We then compare these mean frequencies between small and large error conditions for the different perturbation types.

First, we compare each small or large condition to the aligned baseline condition. For statistical analyses, we implemented a cluster-based permutation t-test. Clusters of time points that exceed the t-value threshold (determined by a t-distribution, given a p-value of 0.05 and sample size of 32) will be shown in light orange or red colors, while clusters of time points that significantly differ from chance after 1000 permutations will be shown in dark orange or red colors.

Theta band
plotPermTestSmallLargeTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestSmallLargeTFRs(freqs = 'theta', roi = 'latcen')

Alpha band
plotPermTestSmallLargeTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestSmallLargeTFRs(freqs = 'alpha', roi = 'latcen')

Beta band
plotPermTestSmallLargeTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestSmallLargeTFRs(freqs = 'beta', roi = 'latcen')

Statistics
getSmallLargeTFRPvalStats(comparison = 'vsAligned', erps = 'frn', roi = 'medfro')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_smallrot             109           159      0.545    0.790    0.065
## 2   1 theta_largerot               0            78      0.000    0.385    0.016
## 3   2 theta_largerot             132           151      0.660    0.750    0.158
## 4   3 theta_smallrdm               0            85      0.000    0.420    0.005
## 5   4 theta_smallrdm             191           201      0.955    1.000    0.247
## 6   5 theta_largerdm               0            69      0.000    0.340    0.026
## 7   6 theta_smallmir              14            66      0.070    0.325    0.071
## 8   7 theta_smallmir             198           201      0.990    1.000    0.221
## 9   8 theta_largemir               0            73      0.000    0.360    0.010
## 10  9 theta_largemir             199           201      0.995    1.000    0.254
## 11 10 alpha_smallrot              NA            NA         NA       NA       NA
## 12 11 alpha_largerot              NA            NA         NA       NA       NA
## 13 12 alpha_smallrdm             133           158      0.665    0.785    0.113
## 14 13 alpha_largerdm              NA            NA         NA       NA       NA
## 15 14 alpha_smallmir              NA            NA         NA       NA       NA
## 16 15 alpha_largemir              99           137      0.495    0.680    0.068
## 17 16  beta_smallrot             108           119      0.540    0.590    0.295
## 18 17  beta_largerot              NA            NA         NA       NA       NA
## 19 18  beta_smallrdm              87           101      0.435    0.500    0.185
## 20 19  beta_largerdm              NA            NA         NA       NA       NA
## 21 20  beta_smallmir              35            63      0.175    0.310    0.081
## 22 21  beta_smallmir              70            78      0.350    0.385    0.306
## 23 22  beta_smallmir              88           116      0.440    0.575    0.087
## 24 23  beta_largemir              NA            NA         NA       NA       NA
getSmallLargeTFRPvalStats(comparison = 'vsAligned', erps = 'frn', roi = 'latcen')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_smallrot             111           165      0.555    0.820    0.047
## 2   1 theta_largerot              14            76      0.070    0.375    0.036
## 3   2 theta_largerot             131           152      0.655    0.755    0.129
## 4   3 theta_smallrdm              13            72      0.065    0.355    0.031
## 5   4 theta_smallrdm             189           201      0.945    1.000    0.217
## 6   5 theta_largerdm              10            38      0.050    0.185    0.124
## 7   6 theta_smallmir              37            63      0.185    0.310    0.154
## 8   7 theta_largemir               0            25      0.000    0.120    0.168
## 9   8 theta_largemir             163           201      0.815    1.000    0.103
## 10  9 alpha_smallrot              NA            NA         NA       NA       NA
## 11 10 alpha_largerot              NA            NA         NA       NA       NA
## 12 11 alpha_smallrdm              NA            NA         NA       NA       NA
## 13 12 alpha_largerdm              NA            NA         NA       NA       NA
## 14 13 alpha_smallmir              41            77      0.205    0.380    0.092
## 15 14 alpha_largemir              64           112      0.320    0.555    0.052
## 16 15  beta_smallrot              65            67      0.325    0.330    0.434
## 17 16  beta_largerot              13            22      0.065    0.105    0.216
## 18 17  beta_smallrdm              NA            NA         NA       NA       NA
## 19 18  beta_largerdm              NA            NA         NA       NA       NA
## 20 19  beta_smallmir              NA            NA         NA       NA       NA
## 21 20  beta_largemir              NA            NA         NA       NA       NA

Time-Frequency Representations (Small vs. Large Difference Waves)

Next, we subtract the aligned condition from each small and large condition, across the different perturbation types. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves

Medial frontal areas

Latercal central areas

Frequency bands comparing difference waves between small and large errors

Theta band
plotPermTestSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

For both regions of interest, we do not find any significant clusters.

Alpha band
plotPermTestSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

For both regions of interest, we do not find any significant clusters.

Beta band
plotPermTestSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We only find a significant cluster for the lateral central ROI, during random rotation training. This occurs around 800 ms post-feedback, where beta power seems to be increased for large errors compared to small errors.

Statistics
getSmallLargeTFRPvalStats(comparison = 'SmallvsLarge', erps = 'frn', roi = 'medfro')
##   X        condition clust_idx_start clust_idx_end time_start time_end p_values
## 1 0 theta_medfro_rot               5            48      0.025    0.235    0.069
## 2 1 theta_medfro_rdm              NA            NA         NA       NA       NA
## 3 2 theta_medfro_mir               0            11      0.000    0.050    0.193
## 4 3 alpha_medfro_rot              NA            NA         NA       NA       NA
## 5 4 alpha_medfro_rdm              NA            NA         NA       NA       NA
## 6 5 alpha_medfro_mir              NA            NA         NA       NA       NA
## 7 6  beta_medfro_rot              NA            NA         NA       NA       NA
## 8 7  beta_medfro_rdm              NA            NA         NA       NA       NA
## 9 8  beta_medfro_mir              NA            NA         NA       NA       NA
getSmallLargeTFRPvalStats(comparison = 'SmallvsLarge', erps = 'frn', roi = 'latcen')
##     X        condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_latcen_rot              NA            NA         NA       NA
## 2   1 theta_latcen_rdm              NA            NA         NA       NA
## 3   2 theta_latcen_mir               0             8       0.00    0.035
## 4   3 alpha_latcen_rot              NA            NA         NA       NA
## 5   4 alpha_latcen_rdm              NA            NA         NA       NA
## 6   5 alpha_latcen_mir              NA            NA         NA       NA
## 7   6  beta_latcen_rot              NA            NA         NA       NA
## 8   7  beta_latcen_rdm              54            73       0.27    0.360
## 9   8  beta_latcen_rdm             104           114       0.52    0.565
## 10  9  beta_latcen_rdm             128           134       0.64    0.665
## 11 10  beta_latcen_rdm             158           183       0.79    0.910
## 12 11  beta_latcen_mir             168           174       0.84    0.865
##    p_values
## 1        NA
## 2        NA
## 3     0.276
## 4        NA
## 5        NA
## 6        NA
## 7        NA
## 8     0.052
## 9     0.191
## 10    0.281
## 11    0.029
## 12    0.452

Time-Frequency Representations (Perturbation Type Comparisons)

Next, we subtract the small from the large error conditions, across the different perturbation types. Then, we compare each perturbation type with the other two. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves.

Medial frontal areas

Latercal central areas

Frequency bands comparing across perturbation types

Theta band
plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

We do not find any significant clusters.

Alpha band
plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

We do not find any significant clusters.

Beta band
plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We find significant clusters in the lateral central ROI, when comparing the fixed rotation and mirror with the random rotation perturbation.

Statistics
getSmallLargeTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'frn', roi = 'medfro')
##     X            condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_medfro_rotvmir              NA            NA         NA       NA
## 2   1 theta_medfro_rotvrdm              NA            NA         NA       NA
## 3   2 theta_medfro_mirvrdm              NA            NA         NA       NA
## 4   3 alpha_medfro_rotvmir              NA            NA         NA       NA
## 5   4 alpha_medfro_rotvrdm              NA            NA         NA       NA
## 6   5 alpha_medfro_mirvrdm              NA            NA         NA       NA
## 7   6  beta_medfro_rotvmir              95           109      0.475    0.540
## 8   7  beta_medfro_rotvrdm              91           115      0.455    0.570
## 9   8  beta_medfro_rotvrdm             124           128      0.620    0.635
## 10  9  beta_medfro_rotvrdm             165           173      0.825    0.860
## 11 10  beta_medfro_rotvrdm             183           190      0.915    0.945
## 12 11  beta_medfro_mirvrdm             168           170      0.840    0.845
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5        NA
## 6        NA
## 7     0.217
## 8     0.059
## 9     0.327
## 10    0.261
## 11    0.286
## 12    0.434
getSmallLargeTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'frn', roi = 'latcen')
##     X            condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_latcen_rotvmir               0             1      0.000    0.000
## 2   1 theta_latcen_rotvrdm              NA            NA         NA       NA
## 3   2 theta_latcen_mirvrdm              NA            NA         NA       NA
## 4   3 alpha_latcen_rotvmir              NA            NA         NA       NA
## 5   4 alpha_latcen_rotvrdm              NA            NA         NA       NA
## 6   5 alpha_latcen_mirvrdm             163           175      0.815    0.870
## 7   6  beta_latcen_rotvmir              NA            NA         NA       NA
## 8   7  beta_latcen_rotvrdm             102           115      0.510    0.570
## 9   8  beta_latcen_rotvrdm             127           132      0.635    0.655
## 10  9  beta_latcen_rotvrdm             155           189      0.775    0.940
## 11 10  beta_latcen_mirvrdm              60            71      0.300    0.350
## 12 11  beta_latcen_mirvrdm             120           135      0.600    0.670
## 13 12  beta_latcen_mirvrdm             158           184      0.790    0.915
##    p_values
## 1     0.297
## 2        NA
## 3        NA
## 4        NA
## 5        NA
## 6     0.193
## 7        NA
## 8     0.168
## 9     0.278
## 10    0.030
## 11    0.227
## 12    0.112
## 13    0.036

Movement preparation

Next, we show TFR plots anad analyses time-locked to the go signal onset. We include time points from -1.5 seconds to zero, but focus our analyses on the second prior to the go signal. That is, once the target is cued until they are allowed to move towards the target.

Early vs. Late training

We split the data into the different conditions, in a similar manner as above.

Time-Frequency Representations

Medial frontal areas

Lateral central areas

Frequency bands compared to aligned baseline

For each ROI, we calculate the mean power (µV²) within each participant of the following frequency bands: theta (6-8 Hz), alpha (9-13 Hz), and beta (13-25 Hz). We then compare these mean frequencies between early and late training for the different perturbation types.

First, we compare each early or late condition to the aligned baseline condition. For statistical analyses, we implemented a cluster-based permutation t-test. Clusters of time points that exceed the t-value threshold (determined by a t-distribution, given a p-value of 0.05 and sample size of 32) will be shown in light orange or red colors, while clusters of time points that significantly differ from chance after 1000 permutations will be shown in dark orange or red colors.

Theta band
plotGoOnsetPermTestEarlyLateTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestEarlyLateTFRs(freqs = 'theta', roi = 'latcen')

Alpha band
plotGoOnsetPermTestEarlyLateTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestEarlyLateTFRs(freqs = 'alpha', roi = 'latcen')

Beta band
plotGoOnsetPermTestEarlyLateTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestEarlyLateTFRs(freqs = 'beta', roi = 'latcen')

Statistics
getEarlyLateTFRPvalStats(comparison = 'vsAligned', erps = 'lrp', roi = 'medfro')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_earlyrot              NA            NA         NA       NA       NA
## 2   1  theta_laterot              27            48     -0.865   -0.765    0.176
## 3   2 theta_earlyrdm              NA            NA         NA       NA       NA
## 4   3  theta_laterdm              NA            NA         NA       NA       NA
## 5   4 theta_earlymir              NA            NA         NA       NA       NA
## 6   5  theta_latemir              50            72     -0.750   -0.645    0.197
## 7   6 alpha_earlyrot              NA            NA         NA       NA       NA
## 8   7  alpha_laterot              NA            NA         NA       NA       NA
## 9   8 alpha_earlyrdm              77           178     -0.615   -0.115    0.007
## 10  9  alpha_laterdm             132           155     -0.340   -0.230    0.146
## 11 10  alpha_laterdm             176           201     -0.120    0.000    0.124
## 12 11 alpha_earlymir             188           201     -0.060    0.000    0.190
## 13 12  alpha_latemir             128           201     -0.360    0.000    0.014
## 14 13  beta_earlyrot              55            58     -0.725   -0.715    0.447
## 15 14  beta_earlyrot             147           181     -0.265   -0.100    0.024
## 16 15   beta_laterot              42            53     -0.790   -0.740    0.194
## 17 16  beta_earlyrdm             168           178     -0.160   -0.115    0.364
## 18 17   beta_laterdm             113           125     -0.435   -0.380    0.221
## 19 18   beta_laterdm             140           201     -0.300    0.000    0.008
## 20 19  beta_earlymir             166           183     -0.170   -0.090    0.182
## 21 20   beta_latemir             140           201     -0.300    0.000    0.004
getEarlyLateTFRPvalStats(comparison = 'vsAligned', erps = 'lrp', roi = 'latcen')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_earlyrot               0            22     -1.000   -0.895    0.166
## 2   1  theta_laterot              18            52     -0.910   -0.745    0.090
## 3   2 theta_earlyrdm              24            41     -0.880   -0.800    0.206
## 4   3  theta_laterdm             111           166     -0.445   -0.175    0.056
## 5   4 theta_earlymir              12            32     -0.940   -0.845    0.173
## 6   5  theta_latemir              38            65     -0.810   -0.680    0.125
## 7   6 alpha_earlyrot              NA            NA         NA       NA       NA
## 8   7  alpha_laterot              NA            NA         NA       NA       NA
## 9   8 alpha_earlyrdm              NA            NA         NA       NA       NA
## 10  9  alpha_laterdm              NA            NA         NA       NA       NA
## 11 10 alpha_earlymir              26            37     -0.870   -0.820    0.244
## 12 11  alpha_latemir             127           201     -0.365    0.000    0.011
## 13 12  beta_earlyrot              78            95     -0.610   -0.530    0.149
## 14 13  beta_earlyrot             166           185     -0.170   -0.080    0.105
## 15 14   beta_laterot              NA            NA         NA       NA       NA
## 16 15  beta_earlyrdm              NA            NA         NA       NA       NA
## 17 16   beta_laterdm              20            39     -0.900   -0.810    0.088
## 18 17   beta_laterdm             118           180     -0.410   -0.105    0.015
## 19 18  beta_earlymir              NA            NA         NA       NA       NA
## 20 19   beta_latemir              30            41     -0.850   -0.800    0.211
## 21 20   beta_latemir             130           201     -0.350    0.000    0.018

Time-Frequency Representations (Early vs. Late Difference Waves)

Next, we subtract the aligned condition from each early and late condition, across the different perturbation types. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves

Medial frontal areas

Latercal central areas

Frequency bands comparing difference waves between early and late training

Theta band
plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

We do not find significant clusters.

Alpha band
plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

We find differences in both ROIs when comparing early and late random perturbation training. In both ROIs, alpha power is decreased in late random training compared to early.

Beta band
plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We find differences in both ROIs when comparing early and late random perturbation training. In both ROIs, beta power is decreased in late random training compared to early.

Statistics
getEarlyLateTFRPvalStats(comparison = 'EarlyvsLate', erps = 'lrp', roi = 'medfro')
##    X        condition clust_idx_start clust_idx_end time_start time_end
## 1  0 theta_medfro_rot              NA            NA         NA       NA
## 2  1 theta_medfro_rdm              NA            NA         NA       NA
## 3  2 theta_medfro_mir              NA            NA         NA       NA
## 4  3 alpha_medfro_rot              NA            NA         NA       NA
## 5  4 alpha_medfro_rdm              22            27      -0.89    -0.87
## 6  5 alpha_medfro_rdm              82           201      -0.59     0.00
## 7  6 alpha_medfro_mir              NA            NA         NA       NA
## 8  7  beta_medfro_rot              NA            NA         NA       NA
## 9  8  beta_medfro_rdm             120           151      -0.40    -0.25
## 10 9  beta_medfro_mir              NA            NA         NA       NA
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5     0.296
## 6     0.006
## 7        NA
## 8        NA
## 9     0.049
## 10       NA
getEarlyLateTFRPvalStats(comparison = 'EarlyvsLate', erps = 'lrp', roi = 'latcen')
##     X        condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_latcen_rot              NA            NA         NA       NA
## 2   1 theta_latcen_rdm              NA            NA         NA       NA
## 3   2 theta_latcen_mir              NA            NA         NA       NA
## 4   3 alpha_latcen_rot              NA            NA         NA       NA
## 5   4 alpha_latcen_rdm             110           174     -0.450   -0.135
## 6   5 alpha_latcen_mir              NA            NA         NA       NA
## 7   6  beta_latcen_rot              NA            NA         NA       NA
## 8   7  beta_latcen_rdm              23            37     -0.885   -0.820
## 9   8  beta_latcen_rdm              71            78     -0.645   -0.615
## 10  9  beta_latcen_rdm             122           154     -0.390   -0.235
## 11 10  beta_latcen_mir              NA            NA         NA       NA
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5     0.034
## 6        NA
## 7        NA
## 8     0.201
## 9     0.332
## 10    0.032
## 11       NA

Time-Frequency Representations (Perturbation type comparisons)

Next, we subtract the early from the late condition, across the different perturbation types. Then, we compare each perturbation type with the other two. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves.

Medial frontal areas

Lateral central areas

Frequency bands comparing across perturbation types

Theta band
plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

We do not observe any significant clusters for both regions of interest.

Alpha band
plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

We do not observe any significant clusters for both regions of interest.

Beta band
plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestPTypeEarlyLateDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We do not observe any significant clusters for both regions of interest.

Statistics
getEarlyLateTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'lrp', roi = 'medfro')
##     X            condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_medfro_rotvmir              NA            NA         NA       NA
## 2   1 theta_medfro_rotvrdm              NA            NA         NA       NA
## 3   2 theta_medfro_mirvrdm              NA            NA         NA       NA
## 4   3 alpha_medfro_rotvmir              NA            NA         NA       NA
## 5   4 alpha_medfro_rotvrdm             102           131     -0.490   -0.350
## 6   5 alpha_medfro_rotvrdm             196           201     -0.020    0.000
## 7   6 alpha_medfro_mirvrdm              19            30     -0.905   -0.855
## 8   7 alpha_medfro_mirvrdm             116           146     -0.420   -0.275
## 9   8  beta_medfro_rotvmir              NA            NA         NA       NA
## 10  9  beta_medfro_rotvrdm              56            68     -0.720   -0.665
## 11 10  beta_medfro_rotvrdm             147           159     -0.265   -0.210
## 12 11  beta_medfro_mirvrdm              NA            NA         NA       NA
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5     0.104
## 6     0.292
## 7     0.233
## 8     0.103
## 9        NA
## 10    0.212
## 11    0.238
## 12       NA
getEarlyLateTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'lrp', roi = 'latcen')
##    X            condition clust_idx_start clust_idx_end time_start time_end
## 1  0 theta_latcen_rotvmir              NA            NA         NA       NA
## 2  1 theta_latcen_rotvrdm              NA            NA         NA       NA
## 3  2 theta_latcen_mirvrdm              NA            NA         NA       NA
## 4  3 alpha_latcen_rotvmir              NA            NA         NA       NA
## 5  4 alpha_latcen_rotvrdm              NA            NA         NA       NA
## 6  5 alpha_latcen_mirvrdm              NA            NA         NA       NA
## 7  6  beta_latcen_rotvmir              NA            NA         NA       NA
## 8  7  beta_latcen_rotvrdm              23            31     -0.885    -0.85
## 9  8  beta_latcen_rotvrdm             140           145     -0.300    -0.28
## 10 9  beta_latcen_mirvrdm              NA            NA         NA       NA
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5        NA
## 6        NA
## 7        NA
## 8     0.289
## 9     0.348
## 10       NA

Small vs. Large errors

We then repeat the same analyses steps but now compare small and large error conditions.

Time-Frequency Representations

Medial frontal areas

Lateral central areas

Frequency bands compared to aligned baseline

Theta band
plotGoOnsetPermTestSmallLargeTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestSmallLargeTFRs(freqs = 'theta', roi = 'latcen')

Alpha band
plotGoOnsetPermTestSmallLargeTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestSmallLargeTFRs(freqs = 'alpha', roi = 'latcen')

Beta band
plotGoOnsetPermTestSmallLargeTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestSmallLargeTFRs(freqs = 'beta', roi = 'latcen')

Statistics
getSmallLargeTFRPvalStats(comparison = 'vsAligned', erps = 'lrp', roi = 'medfro')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_smallrot              NA            NA         NA       NA       NA
## 2   1 theta_largerot              NA            NA         NA       NA       NA
## 3   2 theta_smallrdm              NA            NA         NA       NA       NA
## 4   3 theta_largerdm              NA            NA         NA       NA       NA
## 5   4 theta_smallmir              NA            NA         NA       NA       NA
## 6   5 theta_largemir              NA            NA         NA       NA       NA
## 7   6 alpha_smallrot              NA            NA         NA       NA       NA
## 8   7 alpha_largerot              NA            NA         NA       NA       NA
## 9   8 alpha_smallrdm             180           201     -0.100    0.000    0.156
## 10  9 alpha_largerdm              NA            NA         NA       NA       NA
## 11 10 alpha_smallmir              89           101     -0.555   -0.500    0.216
## 12 11 alpha_smallmir             137           201     -0.315    0.000    0.038
## 13 12 alpha_largemir             178           194     -0.110   -0.035    0.233
## 14 13  beta_smallrot              42            50     -0.790   -0.755    0.235
## 15 14  beta_smallrot             167           190     -0.165   -0.055    0.064
## 16 15  beta_largerot              20           101     -0.900   -0.500    0.005
## 17 16  beta_largerot             138           186     -0.310   -0.075    0.013
## 18 17  beta_smallrdm             166           186     -0.170   -0.075    0.139
## 19 18  beta_largerdm              NA            NA         NA       NA       NA
## 20 19  beta_smallmir             148           201     -0.260    0.000    0.015
## 21 20  beta_largemir             166           188     -0.170   -0.065    0.104
getSmallLargeTFRPvalStats(comparison = 'vsAligned', erps = 'lrp', roi = 'latcen')
##     X      condition clust_idx_start clust_idx_end time_start time_end p_values
## 1   0 theta_smallrot              NA            NA         NA       NA       NA
## 2   1 theta_largerot              NA            NA         NA       NA       NA
## 3   2 theta_smallrdm              NA            NA         NA       NA       NA
## 4   3 theta_largerdm             126           129     -0.370   -0.360    0.223
## 5   4 theta_smallmir              NA            NA         NA       NA       NA
## 6   5 theta_largemir              NA            NA         NA       NA       NA
## 7   6 alpha_smallrot              10            22     -0.950   -0.895    0.246
## 8   7 alpha_largerot             188           201     -0.060    0.000    0.220
## 9   8 alpha_smallrdm             161           201     -0.195    0.000    0.055
## 10  9 alpha_largerdm              NA            NA         NA       NA       NA
## 11 10 alpha_smallmir             170           201     -0.150    0.000    0.075
## 12 11 alpha_largemir             171           200     -0.145   -0.005    0.107
## 13 12  beta_smallrot             118           132     -0.410   -0.345    0.229
## 14 13  beta_largerot              18           100     -0.910   -0.505    0.001
## 15 14  beta_largerot             145           184     -0.275   -0.085    0.057
## 16 15  beta_smallrdm              11            26     -0.945   -0.875    0.222
## 17 16  beta_smallrdm             170           188     -0.150   -0.065    0.160
## 18 17  beta_largerdm              NA            NA         NA       NA       NA
## 19 18  beta_smallmir              23            42     -0.885   -0.795    0.129
## 20 19  beta_smallmir             125           148     -0.375   -0.265    0.107
## 21 20  beta_smallmir             190           201     -0.050    0.000    0.250
## 22 21  beta_largemir             165           190     -0.175   -0.055    0.059

Time-Frequency Representations (Small vs. Large Difference Waves)

Next, we subtract the aligned condition from each small and large condition, across the different perturbation types. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves.

Medial frontal areas

Latercal central areas

Frequency bands comparing difference waves between small and large errors

Theta band
plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

For both regions of interest, we do not find any significant clusters.

Alpha band
plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

We find a significant cluster for random condition in lateral central areas, where small errors are more negative than large errors.

Beta band
plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

We find differences in both ROIs for the rotation perturbation, where large errors show more negative power than small errors.

Statistics
getSmallLargeTFRPvalStats(comparison = 'SmallvsLarge', erps = 'lrp', roi = 'medfro')
##     X        condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_medfro_rot              NA            NA         NA       NA
## 2   1 theta_medfro_rdm              NA            NA         NA       NA
## 3   2 theta_medfro_mir              NA            NA         NA       NA
## 4   3 alpha_medfro_rot              13            25     -0.935   -0.880
## 5   4 alpha_medfro_rot              68            89     -0.660   -0.560
## 6   5 alpha_medfro_rdm              NA            NA         NA       NA
## 7   6 alpha_medfro_mir              NA            NA         NA       NA
## 8   7  beta_medfro_rot              15            28     -0.925   -0.865
## 9   8  beta_medfro_rot              78            87     -0.610   -0.570
## 10  9  beta_medfro_rot             137           160     -0.315   -0.205
## 11 10  beta_medfro_rdm              NA            NA         NA       NA
## 12 11  beta_medfro_mir              89           101     -0.555   -0.500
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4     0.221
## 5     0.170
## 6        NA
## 7        NA
## 8     0.177
## 9     0.259
## 10    0.034
## 11       NA
## 12    0.250
getSmallLargeTFRPvalStats(comparison = 'SmallvsLarge', erps = 'lrp', roi = 'latcen')
##     X        condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_latcen_rot              NA            NA         NA       NA
## 2   1 theta_latcen_rdm              NA            NA         NA       NA
## 3   2 theta_latcen_mir              NA            NA         NA       NA
## 4   3 alpha_latcen_rot               8            22     -0.960   -0.895
## 5   4 alpha_latcen_rdm             146           201     -0.270    0.000
## 6   5 alpha_latcen_mir              NA            NA         NA       NA
## 7   6  beta_latcen_rot              16            97     -0.920   -0.520
## 8   7  beta_latcen_rot             158           173     -0.210   -0.140
## 9   8  beta_latcen_rdm              NA            NA         NA       NA
## 10  9  beta_latcen_mir              26            38     -0.870   -0.815
## 11 10  beta_latcen_mir             109           142     -0.455   -0.295
##    p_values
## 1        NA
## 2        NA
## 3        NA
## 4     0.228
## 5     0.033
## 6        NA
## 7     0.001
## 8     0.226
## 9        NA
## 10    0.271
## 11    0.072

Time-Frequency Representations (Perturbation Type Comparisons)

Next, we subtract the small from the large error conditions, across the different perturbation types. Then, we compare each perturbation type with the other two. Statistical analyses will still be based from the cluster-based permutation tests conducted on these difference waves.

Medial frontal areas

Latercal central areas

Frequency bands comparing across perturbation types

Theta band
plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'medfro')

plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'theta', roi = 'latcen')

We do not find any significant clusters.

Alpha band
plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'medfro')

plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'alpha', roi = 'latcen')

For the lateral central area, we find a difference between fixed rotation and mirror perturbations, where rotation alpha power is decreased compared to mirror, just following the target onset.

Beta band
plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'medfro')

plotGoOnsetPermTestPTypeSmallLargeDiffWavesTFRs(freqs = 'beta', roi = 'latcen')

For the lateral central area, we find a significant cluster when comparing mirror and fixed rotation, occurring just after target onset. Beta power for the fixed rotation is lower compared to mirror. We also observe more negative power for rotation compared to mirror in medial frontal areas, but this occurs later and for a much shorter time.

Statistics
getSmallLargeTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'lrp', roi = 'medfro')
##     X            condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_medfro_rotvmir              NA            NA         NA       NA
## 2   1 theta_medfro_rotvrdm              NA            NA         NA       NA
## 3   2 theta_medfro_mirvrdm             130           154     -0.350   -0.235
## 4   3 alpha_medfro_rotvmir               9            32     -0.955   -0.845
## 5   4 alpha_medfro_rotvmir              62           107     -0.690   -0.470
## 6   5 alpha_medfro_rotvmir             156           164     -0.220   -0.185
## 7   6 alpha_medfro_rotvrdm              11            30     -0.945   -0.855
## 8   7 alpha_medfro_mirvrdm              NA            NA         NA       NA
## 9   8  beta_medfro_rotvmir              13            41     -0.935   -0.800
## 10  9  beta_medfro_rotvmir              53            64     -0.735   -0.685
## 11 10  beta_medfro_rotvmir              76            99     -0.620   -0.510
## 12 11  beta_medfro_rotvmir             134           164     -0.330   -0.185
## 13 12  beta_medfro_rotvrdm             139           151     -0.305   -0.250
## 14 13  beta_medfro_mirvrdm              NA            NA         NA       NA
##    p_values
## 1        NA
## 2        NA
## 3     0.134
## 4     0.151
## 5     0.060
## 6     0.271
## 7     0.160
## 8        NA
## 9     0.061
## 10    0.214
## 11    0.095
## 12    0.038
## 13    0.187
## 14       NA
getSmallLargeTFRPvalStats(comparison = 'PerturbTypeComp', erps = 'lrp', roi = 'latcen')
##     X            condition clust_idx_start clust_idx_end time_start time_end
## 1   0 theta_latcen_rotvmir              NA            NA         NA       NA
## 2   1 theta_latcen_rotvrdm              59            63     -0.705   -0.690
## 3   2 theta_latcen_rotvrdm             123           137     -0.385   -0.320
## 4   3 theta_latcen_mirvrdm             128           147     -0.360   -0.270
## 5   4 alpha_latcen_rotvmir               7            66     -0.965   -0.675
## 6   5 alpha_latcen_rotvrdm               1            30     -0.995   -0.855
## 7   6 alpha_latcen_mirvrdm              NA            NA         NA       NA
## 8   7  beta_latcen_rotvmir              13            75     -0.935   -0.630
## 9   8  beta_latcen_rotvmir              85            98     -0.575   -0.515
## 10  9  beta_latcen_rotvmir             135           163     -0.325   -0.190
## 11 10  beta_latcen_rotvmir             199           201     -0.005    0.000
## 12 11  beta_latcen_rotvrdm              15            30     -0.925   -0.855
## 13 12  beta_latcen_rotvrdm              34            41     -0.830   -0.800
## 14 13  beta_latcen_rotvrdm             141           155     -0.295   -0.230
## 15 14  beta_latcen_rotvrdm             164           173     -0.180   -0.140
## 16 15  beta_latcen_mirvrdm              30            42     -0.850   -0.795
## 17 16  beta_latcen_mirvrdm             111           127     -0.445   -0.370
##    p_values
## 1        NA
## 2     0.259
## 3     0.216
## 4     0.199
## 5     0.023
## 6     0.081
## 7        NA
## 8     0.007
## 9     0.232
## 10    0.092
## 11    0.413
## 12    0.190
## 13    0.380
## 14    0.232
## 15    0.360
## 16    0.292
## 17    0.190

Manuscript plots

Although we have all figures and statistical comparisons in this document, we summarize the main findings in the manuscript.

plotTFRThetaResults()

plotTFRAlphaResults()

plotTFRBetaResults()